Towards secure distributed medical diagnosis: a blockchain-driven MCAU-Net and MAA-Bi-LSTM framework

Abstract

Lung cancer is considered as the deadliest type of cancer, a good diagnosis as well as effective treatment is based on early detection. By utilizing Deep Learning (DL) techniques, the medical practitioner’s burden is reduced since these models can automate the diagnosis and disease classification. However, these models often suffer from modest accuracy and scalability issues, which affect the model’s disease detection performance. Consequently, a Federated Learning (FL)-based Global Data Trained Disease Detection (GDTDD) model, to provide better disease detection in hospitals. Initially, obtained local data (lung CT images) from different hospitals were pre-processed using a median filtering technique. Then these pre-processed images get segmented with the utilization of a multi-Convolutional layer-assisted U-Net (MCAU-Net) model. From these segmented images, features including Multi-texton, Median Binary Pattern (MBP) and Improved Local Gabor Transitional Pattern (LGTrP) features were extracted. Then these extracted features were fused utilizing multi-scale channel attention module (MS-CAM) based feature fusion technique. Then, trained the hybrid Recurrent Neural Network (RNN) and Modified Attention layer Assisted Bidirectional Long Short-Term Memory (MAA-Bi-LSTM) with this fused feature and obtained the local model as output. Afterwards, these local models were transmitted to Federated Learning, which merges and aggregates these locally trained models and provides a global trained model. This globally trained model is stored on the blockchain to ensure security and privacy. Then this global model is trained with fused features for accurate and effective disease detection. Moreover, this MAA-Bi-LSTM+RNN model’s effectiveness in disease detection is proven by the experimental findings. The Precision and F-measure values of the proposed model are 95.48% and 96.30%